This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of theService Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see thelaunch stage descriptions.

This page introduces how to build LLM-powered applications usingLangChain. The overviews on thispage link to procedure guides in GitHub.

What is LangChain?

LangChain is an LLM orchestration framework that helps developers buildgenerative AI applications or retrieval-augmented generation (RAG) workflows. Itprovides the structure, tools, and components to streamline complex LLMworkflows.

For more information about LangChain, see theGoogleLangChain page. For more information about the LangChain framework, see theLangChain product documentation.

LangChain components for Cloud SQL for SQL Server

Cloud SQL for SQL Server offers the following LangChain interfaces:

• Document loader
• Chat message history

Learn how to use LangChain with theLangChain Quickstart for Cloud SQL for SQL Server.

Document loader for Cloud SQL for SQL Server

The document loader saves, loads, and deletes a LangChainDocument objects. For example, you can load data for processing intoembeddings and either store it in vector store or use it as a tool to providespecific context to chains.

To load documents from document loader in Cloud SQL for SQL Server, use theMSSQLLoader class.MSSQLLoader methods return one or more documents from atable. Use theMSSQLDocumentSaver class to save and delete documents.

For more information, see theLangChain Documentloaders topic.

Document loader procedure guide

TheCloud SQL for SQL Server guide for documentloader shows you how to do the following:

• Install the integration package and LangChain
• Load documents from a table
• Add a filter to the loader
• Customize the connection and authentication
• Customize Document construction by specifying customer content and metadata
• How to use and customize aMSSQLDocumentSaver to store and delete documents

Chat message history for Cloud SQL for SQL Server

Question and answer applications require a history of the things said in theconversation to give the application context for answering further questionsfrom the user. The LangChainChatMessageHistory class lets the applicationsave messages to a database and retrieve them when needed to formulate furtheranswers. A message can be a question, an answer, a statement, a greeting or anyother piece of text that the user or application gives during the conversation.ChatMessageHistory stores each message and chains messages together for eachconversation.

Cloud SQL for SQL Server extends this class withMSSQLChatMessageHistory.

Chat message history procedure guide

TheCloud SQL for SQL Server guide for chat messagehistory shows you how to do the following:

• Install LangChain and authenticate to Google Cloud
• Create aMSSQLEngine object and configure a connection pool to yourCloud SQL for SQL Server database
• Initialize a table
• Initialize theMSSQLChatMessageHistory class to add and delete messages
• Create a chain for message history using the LangChain Expression Language(LCEL) and Google's Vertex AI chat models